CA2555080A1 - Systems and methods for automated diagnosis and decision support for heart related diseases and conditions - Google Patents

Systems and methods for automated diagnosis and decision support for heart related diseases and conditions Download PDF

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Publication number
CA2555080A1
CA2555080A1 CA002555080A CA2555080A CA2555080A1 CA 2555080 A1 CA2555080 A1 CA 2555080A1 CA 002555080 A CA002555080 A CA 002555080A CA 2555080 A CA2555080 A CA 2555080A CA 2555080 A1 CA2555080 A1 CA 2555080A1
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Prior art keywords
image data
automatically
features
patient
heart
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CA002555080A
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French (fr)
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CA2555080C (en
Inventor
Sriram Krishnan
Alok Gupta
R. Bharat Rao
Dorin Comaniciu
Xiang Sean Zhou
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Siemens Corp
Siemens Medical Solutions USA Inc
Original Assignee
Siemens Medical Solutions Usa, Inc.
Siemens Corporate Research, Inc.
Sriram Krishnan
Alok Gupta
R. Bharat Rao
Dorin Comaniciu
Xiang Sean Zhou
Siemens Corporation
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Application filed by Siemens Medical Solutions Usa, Inc., Siemens Corporate Research, Inc., Sriram Krishnan, Alok Gupta, R. Bharat Rao, Dorin Comaniciu, Xiang Sean Zhou, Siemens Corporation filed Critical Siemens Medical Solutions Usa, Inc.
Publication of CA2555080A1 publication Critical patent/CA2555080A1/en
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Biomedical Technology (AREA)
  • Public Health (AREA)
  • Epidemiology (AREA)
  • Pathology (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Ultra Sonic Daignosis Equipment (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

CAD (computer-aided diagnosis) systems and applications for cardiac imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated assessment of regional myocardial function through wall motion analysis, automated diagnosis of heart diseases and conditions such as cardiomyopathy, coronary artery disease and other heart-related medical conditions, and other automated decision support functions. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to "learn" to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.

Claims (40)

1. A method for providing automatic diagnosis and decision support for cardiac imaging, comprising:
obtaining information from image data of a heart of a patient;
obtaining information from non-image data records of the patient; and automatically assessing a condition of the heart using the obtained information.
2. The method of claim 1, wherein obtaining information from image data comprises automatically extracting one or more features from ultrasound image data in one or more ultrasound imaging modalities.
3. The method of claim 2, wherein the ultrasound image data comprises 3D ultrasound image data.
4. The method of claim 2, wherein automatically extracting one or more features from ultrasound image data comprises extracting global features of heart function, including left ventricular volume, left ventricular ejection fraction, left ventricular wall thickness, left ventricular wall mass, or diastolic function indicators such as the E/A ratio, or any combination of said global features.
5. The method of claim 2, wherein automatically extracting one or more features from ultrasound image data comprises extracting regional parameters, including tissue velocity data, strain data, strain rate data, perfusion data, or timing data, or any combination of said regional parameters.
6. The method of claim 5, wherein automatically extracting one or more features from ultrasound image data comprises extracting features related to strain, fluid motion such as acoustic streaming, blood motion such as contrast perfusion, or B-mode images, or any combination thereof.
7. The method of claim 1, wherein the image data comprises one of ultrasound image data, MR (magnetic resonance) image data, CT (computed tomography) image data, PET image data, nuclear medicine image data, or image data of a coronary artery tree, or any combination thereof.
8. The method of claim l, wherein obtaining information from non-image data records comprises automatically extracting features from structured and/or unstructured data sources comprising clinical, family and/or history information for the subject patient.
9. The method of claim 1, wherein obtaining information from image data of the heart comprises performing an automated wall motion analysis using image features to obtain information related to regional myocardial function of the heart.
10. The method of claim 9, wherein performing an automated wall motion analysis comprises automatically extracting using image features related to myocardial wall motion and myocardial wall thickening from the image data.
11. The method of claim 1, wherein automatically assessing a condition of the heart comprises automatically determining a probability of diagnosis of a heart disease or condition.
12. The method of claim 11, further comprising automatically determining a confidence of said probability of diagnosis.
13. The method of claim 12, further comprising automatically determining additional information which would increase the confidence of said probability of diagnosis.
14. The method of claim 13, wherein automatically determining additional information further comprises determining a measure of usefulness of said additional information in increasing the confidence of said probability of diagnosis.
15. The method of claim 11, further comprising automatically identifying one or more previously diagnosed cases that are similar to a current diagnosis.
16. The method of claim 15, wherein automatically identifying one or more previously diagnosed cases that are similar to a current diagnosis comprises using the obtained information to search a library of labeled cases with features similar to the obtained information.
17. The method of claim 16, comprising displaying the identified cases.
18. The method of claim 1, wherein automatically assessing a condition of the heart using the obtained information comprises classifying a condition of the heart using a machine learning method, a model-based method, or any combination of machine learning and model-based methods, that is trained to analyze the obtained information.
19. The method of claim 18, comprising retraining the method for classifying on a continual or periodic basis using expert data and/or data learned from a plurality of case studies.
20. A method for providing automated diagnostic and decision support for cardiac imaging, comprising:
automatically extracting features from patient data of a subject patient, the patient data comprising image data and non-image data, and automatically determining a current state of a heart of the subject patient by analyzing the features extracted from the patient data of the subject patient;
and automatically providing decision support to assist physician workflow regarding a healthcare or diagnostic or therapeutic path for the subject patient, based on a determined current state of the subject patient.
21. The method of claim 20, wherein automatically determining a current state of the subject patient comprises automatically determining a probability of diagnosis of a heart disease or condition or the probability of developing a heart disease or condition in the future.
22. The method of claim 21, wherein automatically providing decision support comprises automatically determining one or more additional features of patient which would increase a confidence of said probability of diagnosis.
23. The method of claim 22, wherein automatically determining one or more additional features further comprises determining for each of said one or more additional features, a measure of usefulness in increasing said confidence of diagnosis.
24. The method of claim 21, wherein automatically providing decision support comprises automatically determining one or more additional cardiac imaging tests which would increase a confidence of said probability of diagnosis.
25. The method of claim 24, wherein automatically determining one or more additional cardiac imaging tests further comprises determining for each of said one or more additional tests, a measure of usefulness in increasing said confidence of diagnosis.
26. The method of claim 21, wherein automatically providing decision support comprises automatically identifying one or more previously diagnosed cases that are similar to the current case.
27. The method of claim 26, comprising displaying the one or more identified similar cases.
28. The method of claim 20, wherein automatically determining a current state of the subject patient comprises automatically determining a likelihood of the subject patient developing a heart disease or condition.
29. The method of claim 20, wherein automatically providing decision support to assist physician workflow comprises suggesting a therapy.
30. The method of claim 29, further comprising automatically providing a probability and/or measure of confidence that the therapy will have a determined outcome.
31. The method of claim 29, further comprising automatically providing a probability and/or measure of confidence that the therapy will not have a determined detrimental impact such as side effects.
32. The method of claim 30, wherein the probability is a distribution over possible outcomes both beneficial and detrimental
33. The method of claim 30, wherein the probability is a set of distributions over possible outcomes both beneficial and detrimental at one or more time points in the future.
34. The method of claim 30, wherein the probability is a time-varying distribution over possible outcomes at different times in the future.
35. A method for providing automatic diagnosis and decision support for cardiac imaging, comprising:
obtaining information from image data of a heart of a patient from at least two different imaging modalities, including ultrasound, MR, CT, PET, MR
angiography, CT angiography, X-ray angiography, and nuclear medicine;

extracting features from the image data of the at least two imaging modalities, automatically determining a current state of the patient by analyzing the extracted features; and automatically providing decision support to assist physician workflow regarding a healthcare or diagnostic or therapeutic path for the patient, based on a determined current state of the subject patient.
36. The method of claim 35, where the extracted features from the image data are combined with features from non-imaging data of the patient.
37 The method of claim 29, where one of the imaging modalities is used to image coronary arteries.
38 The method of claim 37, wherein CT angiography is used to extract features on coronary arteries, and one or more of CT, MR, PET, nuclear medicine, and ultrasound is used to image the myocardium of the heart.
39. The method of claim 37, wherein MR angiography is used to extract features on coronary arteries, and one or more of CT, MR, PET, nuclear medicine, and ultrasound is used to image the myocardium of the heart.
40 The method of claim 37, wherein X-ray angiography is used to extract features on coronary arteries, and one or more of CT, MR, PET, nuclear medicine, and ultrasound is used to image the myocardium of the heart.
CA2555080A 2004-02-03 2004-12-06 Systems and methods for automated diagnosis and decision support for heart related diseases and conditions Expired - Fee Related CA2555080C (en)

Applications Claiming Priority (5)

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US54136004P 2004-02-03 2004-02-03
US60/541,360 2004-02-03
US10/876,801 2004-06-25
US10/876,801 US7912528B2 (en) 2003-06-25 2004-06-25 Systems and methods for automated diagnosis and decision support for heart related diseases and conditions
PCT/US2004/040757 WO2005081168A2 (en) 2004-02-03 2004-12-06 Systems and methods for automated diagnosis and decision support for heart related diseases and conditions

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CA2555080C CA2555080C (en) 2012-04-24

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US (1) US7912528B2 (en)
EP (1) EP1711908B1 (en)
JP (1) JP2007527743A (en)
CN (1) CN1914617A (en)
AU (1) AU2004316027B2 (en)
CA (1) CA2555080C (en)
WO (1) WO2005081168A2 (en)

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